Facilitating the Manual Annotation of Sounds When Using Large Taxonomies
By Xavier Favory, Eduardo Fonseca, Frederic Font, Xavier Serra. 2018
7 citations recorded by the source corpus.
Properly annotated multimedia content is crucial for supporting advances in many Information Retrieval applications. It enables, for instance, the development of automatic tools for the annotation of large and diverse multimedia collections. In the context of everyday sounds and online collections, the content to describe is very diverse and involves many different types of concepts, often organised in large hierarchical structures called taxonomies. This makes the task of manually annotating content arduous. In this paper, we present our user-centered development of two tools for the manual annotation of audio content from a wide range of types. We conducted a preliminary evaluation of functional prototypes involving real users. The goal is to evaluate them in a real context, engage in discussions with users, and inspire new ideas. A qualitative analysis was carried out including usability questionnaires and semi-structured interviews. This revealed interesting aspects to consider when developing tools for the manual annotation of audio content with labels drawn from large hierarchical taxonomies.
Subfields: Artificial Intelligence, Developmental Biology, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Xavier Serra
- Eduardo Fonseca
- Jordi Pons
- Xavier Favory
- Frederic Font
- D. Ellis
- Manoj Plakal
- Tuomas Virtanen
In Developmental Biology
- Xavier Serra
- Jordi Pons
- Tuomas Virtanen
- D. Ellis
- Eduardo Fonseca
- Xavier Favory
- Manoj Plakal
- Frederic Font
In Signal Processing
- Xavier Serra
- Eduardo Fonseca
- Frederic Font
- Xavier Favory
- Jordi Pons
- D. Ellis
- Manoj Plakal
- Konstantinos Drossos
Citations in the corpus
- FSD50K: An Open Dataset of Human-Labeled Sound Events (2020, methodology): “based on a previous version described in [71].”
- COALA: Co-Aligned Autoencoders for Learning Semantically Enriched Audio Representations (2020, background): “Correspondence to: Xavier Favory <xavier.favory@upf.edu>. audio signals (Favory et al., 2018).”
- Audio tagging with noisy labels and minimal supervision (2019, methodology): “As a final step, the test set was further annotated using a label generation tool [24], in which i) pre-existent labels can be re-validated, and ii) potentially missing labels can be added through exploration of the AudioSet Ontology.”